7 Joins

7.1 Dependencies

Code

library(dplyr)
library(readr)

7.2 Get data

Code

data_unique_id_subset <- read_csv("../data/raw/data_unique_id_subset.csv")
data_age_gender_subset <- read_csv("../data/raw/data_age_gender_subset.csv")
data_amp_summary_subset <- read_csv("../data/raw/data_amp_summary_subset.csv")
data_selfreport_summary_subset <- read_csv("../data/raw/data_selfreport_summary_subset.csv")

nrow(data_unique_id_subset)

[1] 92

Code

nrow(data_age_gender_subset)

[1] 90

Code

nrow(data_amp_summary_subset)

[1] 31

Code

nrow(data_selfreport_summary_subset)

[1] 76

7.3 Practicing joins

Using the data frames below and functions from the _join family, write code to do the following joins.

7.3.1 Practice 1

create ‘data_combined’ by joining data_amp_summary_subset and data_age_gender_subset so that unique_ids in either data frame are retained. which join is this? implement it.

Code

# data_combined <-

7.3.2 Practice 2

create ‘data_self_reports_and_their_amp_data’ by joining data_selfreport_summary_subset and data_amp_summary_subset so that all participants have self-report data, + AMP data if available. which join is this? implement it.

Code

# data_self_reports_and_their_amp_data <-

7.3.3 Practice 3

do the opposite: create ‘data_amp_data_and_their_self_reports’ by joining data_amp_summary_subset and data_selfreport_summary_subset so that all participants have AMP data, + self-report data if available. which join is this? implement it.

Code

# data_amp_data_and_their_self_reports <-

7.3.4 Practice 4

create data_combined_2 by joining ‘data_combined’ and data_selfreport_summary_subset only unique_ids already present in data_combined are retained. which join is this? implement it.

Code

# data_combined_2 <-

7.3.5 Practice 5

create ‘data_missing_ids’ which should list the unique_ids are missing from data_unique_id_subset but are present in at least one of data_age_gender_subset, data_amp_summary_subset, and data_selfreport_summary_subset. This will require two different joins. Which? Implement them.

Code

# data_missing_ids <-

---
title: "Joins"
format:
  html:
    toc: true
    toc_float: true
    code-fold: show
    code-tools: true
    self-contained: true
---

```{r}
#| include: false

# settings, placed in a chunk that will not show in the .html file (because include=FALSE) 

# disables scientific notation so that small numbers appear as eg "0.00001" rather than "1e-05"
options(scipen = 999)  

```

## Dependencies

```{r}
library(dplyr)
library(readr)

```

## Get data

```{r}
data_unique_id_subset <- read_csv("../data/raw/data_unique_id_subset.csv")
data_age_gender_subset <- read_csv("../data/raw/data_age_gender_subset.csv")
data_amp_summary_subset <- read_csv("../data/raw/data_amp_summary_subset.csv")
data_selfreport_summary_subset <- read_csv("../data/raw/data_selfreport_summary_subset.csv")

nrow(data_unique_id_subset)
nrow(data_age_gender_subset)
nrow(data_amp_summary_subset)
nrow(data_selfreport_summary_subset)

```

## Practicing joins

Using the data frames below and functions from the \_join family, write code to do the following joins.

### Practice 1

create 'data_combined' by joining data_amp_summary_subset and data_age_gender_subset so that unique_ids in either data frame are retained. which join is this? implement it.

```{r}
# data_combined <- 

```

### Practice 2

create 'data_self_reports_and_their_amp_data' by joining data_selfreport_summary_subset and data_amp_summary_subset so that all participants have self-report data, + AMP data if available. which join is this? implement it.

```{r}
# data_self_reports_and_their_amp_data <- 

```

### Practice 3

do the opposite: create 'data_amp_data_and_their_self_reports' by joining data_amp_summary_subset and data_selfreport_summary_subset so that all participants have AMP data, + self-report data if available. which join is this? implement it.

```{r}
# data_amp_data_and_their_self_reports <- 

```

### Practice 4

create data_combined_2 by joining 'data_combined' and data_selfreport_summary_subset only unique_ids already present in data_combined are retained. which join is this? implement it.

```{r}
# data_combined_2 <- 

```

### Practice 5

create 'data_missing_ids' which should list the unique_ids are missing from data_unique_id_subset but are present in at least one of data_age_gender_subset, data_amp_summary_subset, and data_selfreport_summary_subset. This will require two different joins. Which? Implement them.

```{r}
# data_missing_ids <- 

```